Moore, Jill EPratt, Henry EPurcaro, Michael J.Weng, Zhiping2022-08-232022-08-232020-01-222020-02-18<p>Moore JE, Pratt HE, Purcaro MJ, Weng Z. A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods. Genome Biol. 2020 Jan 22;21(1):17. doi: 10.1186/s13059-019-1924-8. PMID: 31969180; PMCID: PMC6977301. <a href="https://doi.org/10.1186/s13059-019-1924-8">Link to article on publisher's site</a></p>1474-7596 (Linking)10.1186/s13059-019-1924-831969180https://hdl.handle.net/20.500.14038/41333BACKGROUND: Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. RESULTS: To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types. CONCLUSIONS: Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.en-US© The Author(s). 2020 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.http://creativecommons.org/licenses/by/4.0/BenchmarkEnhancerGenomic interactionsMachine learningTarget geneTranscriptional regulationBioinformaticsComputational BiologyGenetic PhenomenaGenomicsJournal Articlehttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=5137&amp;context=oapubs&amp;unstamped=1https://escholarship.umassmed.edu/oapubs/411816574430oapubs/4118